1. Field of the Invention
The present invention relates generally to signal processing, and particularly to multi-channel digital signal processing.
2. Technical Background
Radar, sonar, and other communication systems, such as spread spectrum communication systems, are sophisticated systems configured to detect and interpret transmitted and/or reflected signals propagating in a communication channel. Radar and other radio frequency (RF) systems employ antennas to capture electromagnetic signals having predetermined transmission characteristics. Sonar systems include hydrophonic sensors for sensing acoustic signals propagating in a marine environment. Signals propagating in such communication channels may be reflected, attenuated, and affected by other transmission path characteristics. As such, a narrow, rectangular pulse transmitted by a signal source typically arrives at the receiver as a relatively wide, attenuated, and rounded signal, corrupted by noise. Furthermore, a single transmitted pulse may arrive at the receiver as two or more multi-path signals displaced in time from each other. The front-end receiver, therefore, must be able to recover a clean version of the transmitted signal from a received signal which has been distorted and corrupted by noise and from multipath signals. In doing so, the front-end receiver provides a signal at some intermediate frequency (IF). The IF signal typically includes many of the channel-induced distortions, including the multipath signals described above. Subsequently, the effects of the channel-induced distortions are mitigated using a process generally referred to as adaptive equalization.
In one approach that has been considered, a receiver is implemented using a multipath time delay and correlation bandwidth analyzer. A signal received by the receiver is correlated with a selected reference signal generated at the receiver. The correlator will generate two or more correlation pulses or maxima, also displaced in time, if the received signal contains strong multi-path contributions. The reference signal may be a time-delayed replica of the received signal.
In another approach that has been considered, an adaptive array of spaced-apart antennas is provided. Each antenna signal is processed identically. Each processing element includes a band pass filter, a local oscillator, a signal mixer and a tapped time delay line. The processed signals are adaptively weighted by a feedback loop and added together to provide a signal with reduced multipath contributions. In yet another approach that has been considered, a multipath receiver apparatus is configured to compare a time-delayed replica of a transmitted signal with the received signal. A signal propagation time delay is selected to maximize the correlation signal.
While the approaches discussed above have their advantages and drawbacks, modem adaptive equalization schemes are typically implemented in software and executed by a digital signal processor (DSP). At the heart of any equalizer is one or more adaptive filters, which are easily implemented in software. Adaptive filters may be used for noise cancellation, echo cancellation, beam forming, in addition to equalization.
Referring to FIG. 1, a conventional DSP-based multi-channel radar receiver 1 is shown. In particular, receiver 1 is shown as a two-channel system. Antenna 2 and front end receiver 3 sense and detect RF signals propagating in the environment. Receiver 3 directs analog signal h1(t) into analog-to-digital (A/D) converter 4. The A/D converter 4 samples the amplitude of the analog signal at discrete time intervals and the resultant digital values are stored in a memory buffer for subsequent processing. In one embodiment, receiver 3 provides an IF signal, the frequency of which, is one-fourth that of the sampling frequency of A/D converter 4. Subsequently, the digital data h1[n] representing signal h1(t) is directed into Discrete Hilbert Transform (DHT) filter 5. The output of DHT filter 5 is a stream of complex signal samples, i.e., the in-phase (I) and quadrature (Q) components of h1[n] shifted in frequency to baseband. Those skilled in the art will recognize that the I-component and the Q-component have the same frequency but differ in phase by 90°. Essentially, DHT 5 is implemented in a DSP by a pair of tapped-delay line or finite impulse response (FIR) bandpass filters. The output of the DHT is decimated, i.e. only every nth sample of the output is used, effectively shifting the frequency to baseband by means of aliasing.
Next, the quadrature components are directed into adaptive equalizer (AE) filter 6 to facilitate later clutter and/or interference cancellation. If the radar employs a phased array antenna, time delay filter 7 may be used to implement time-delay steering. Finally, the filtered I, Q signals are directed into pulse compressor 8. In this block, the signals are correlated with a signal reference to obtain pulse compression. A more detailed diagram of pulse compression filter 8 is shown in FIG. 2.
Referring to FIG. 2, pulse compressor 8 is implemented in the frequency domain. A correlation is performed in the time-domain by a convolution operation. However, those of ordinary skill in the art understand that a convolution in the time domain corresponds to a multiplication in the frequency domain. Accordingly, a correlation function is easily implemented in the frequency domain for the above stated reasons and the I, Q components are directed into a fast Fourier transform module 800 to obtain the spectral representation of the filtered I, Q components. The correlation is then calculated by multiplying I(f)+jQ(f) by the reference signal. Finally, the time domain representation of the pulse compression output is obtained by performing an inverse fast Fourier Transform (IFFT) 804. Note that in the above discussion, only channel (1) one has been discussed. However, channel (2) two operations are identical.
One drawback to the approach described above and illustrated in FIG. 1 and FIG. 2 relates to the relative inefficiency of the design. For example, an FFT (see FIG. 2) must be implemented for each channel, as well as multiple filters. What is needed is a system and method for making multi-channel signal processing more efficient.